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Dynamic Neural Field Theory for Motion Perception

  • Martin A. Giese

Table of contents

  1. Front Matter
    Pages i-xix
  2. Introduction

    1. Martin A. Giese
      Pages 1-5
  3. Basic Concepts

    1. Front Matter
      Pages 7-7
    2. Martin A. Giese
      Pages 9-28
    3. Martin A. Giese
      Pages 49-63
  4. Model for Motion Perception

  5. Other Applications of Neural Fields

    1. Front Matter
      Pages 155-155
    2. Martin A. Giese
      Pages 173-199
    3. Martin A. Giese
      Pages 201-207
  6. Back Matter
    Pages 209-257

About this book

Introduction

Dynamic Neural Field Theory for Motion Perception provides a new theoretical framework that permits a systematic analysis of the dynamic properties of motion perception.
This framework uses dynamic neural fields as a key mathematical concept. The author demonstrates how neural fields can be applied for the analysis of perceptual phenomena and its underlying neural processes. Also, similar principles form a basis for the design of computer vision systems as well as the design of artificially behaving systems. The book discusses in detail the application of this theoretical approach to motion perception and will be of great interest to researchers in vision science, psychophysics, and biological visual systems.

Keywords

algorithms computer vision perception

Authors and affiliations

  • Martin A. Giese
    • 1
    • 2
  1. 1.Institut für NeuroinformatikRuhr-Universität BochumBochumGermany
  2. 2.Artificial Intelligence LaboratoryMassachusetts Institute of TechnologyCambridgeUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4615-5581-0
  • Copyright Information Kluwer Academic Publishers 1999
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4613-7553-1
  • Online ISBN 978-1-4615-5581-0
  • Series Print ISSN 0893-3405
  • Buy this book on publisher's site